Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations38400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 MiB
Average record size in memory231.5 B

Variable types

Text2
Numeric15
Categorical9

Alerts

dataset is uniformly distributed Uniform
dataset_label is uniformly distributed Uniform
question_id has 480 (1.2%) zeros Zeros
cr has 2838 (7.4%) zeros Zeros
std_LOF has 10160 (26.5%) zeros Zeros
completeness has 13705 (35.7%) zeros Zeros
mean_relevance has 2838 (7.4%) zeros Zeros

Reproduction

Analysis started2025-05-22 18:06:27.435013
Analysis finished2025-05-22 18:06:44.403879
Duration16.97 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

file
Text

Distinct120
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:44.482898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length20
Mean length16.816667
Min length12

Characters and Unicode

Total characters645760
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS_16_0.1_BAAI
2nd rowS_16_0.1_BAAI
3rd rowS_16_0.1_BAAI
4th rowS_16_0.1_BAAI
5th rowS_16_0.1_BAAI
ValueCountFrequency (%)
s_16_0.5_intfloat 2080
 
5.4%
w_256_0.25_baai 2080
 
5.4%
w_256_0.1_intfloat 2080
 
5.4%
w_256_0.25_intfloat 2080
 
5.4%
s_16_0.5_deeppavlov 2080
 
5.4%
s_4_0.5_thenlper 2080
 
5.4%
s_8_0.5_deeppavlov 2080
 
5.4%
s_8_0.5_thenlper 2080
 
5.4%
s_32_0.5_baai 2080
 
5.4%
s_16_0.5_baai 2080
 
5.4%
Other values (110) 17600
45.8%
2025-05-22T21:06:44.672785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 115200
17.8%
0 42240
 
6.5%
5 39680
 
6.1%
. 38400
 
5.9%
e 34560
 
5.4%
2 31360
 
4.9%
t 29760
 
4.6%
l 27840
 
4.3%
S 23040
 
3.6%
1 21760
 
3.4%
Other values (19) 241920
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 645760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 115200
17.8%
0 42240
 
6.5%
5 39680
 
6.1%
. 38400
 
5.9%
e 34560
 
5.4%
2 31360
 
4.9%
t 29760
 
4.6%
l 27840
 
4.3%
S 23040
 
3.6%
1 21760
 
3.4%
Other values (19) 241920
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 645760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 115200
17.8%
0 42240
 
6.5%
5 39680
 
6.1%
. 38400
 
5.9%
e 34560
 
5.4%
2 31360
 
4.9%
t 29760
 
4.6%
l 27840
 
4.3%
S 23040
 
3.6%
1 21760
 
3.4%
Other values (19) 241920
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 645760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 115200
17.8%
0 42240
 
6.5%
5 39680
 
6.1%
. 38400
 
5.9%
e 34560
 
5.4%
2 31360
 
4.9%
t 29760
 
4.6%
l 27840
 
4.3%
S 23040
 
3.6%
1 21760
 
3.4%
Other values (19) 241920
37.5%

question_id
Real number (ℝ)

Zeros 

Distinct80
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.5
Minimum0
Maximum79
Zeros480
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-22T21:06:44.756428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.95
Q119.75
median39.5
Q359.25
95-th percentile75.05
Maximum79
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.092507
Coefficient of variation (CV)0.58462043
Kurtosis-1.2003751
Mean39.5
Median Absolute Deviation (MAD)20
Skewness0
Sum1516800
Variance533.26389
MonotonicityNot monotonic
2025-05-22T21:06:44.847505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 480
 
1.2%
1 480
 
1.2%
58 480
 
1.2%
57 480
 
1.2%
56 480
 
1.2%
55 480
 
1.2%
54 480
 
1.2%
53 480
 
1.2%
52 480
 
1.2%
51 480
 
1.2%
Other values (70) 33600
87.5%
ValueCountFrequency (%)
0 480
1.2%
1 480
1.2%
2 480
1.2%
3 480
1.2%
4 480
1.2%
5 480
1.2%
6 480
1.2%
7 480
1.2%
8 480
1.2%
9 480
1.2%
ValueCountFrequency (%)
79 480
1.2%
78 480
1.2%
77 480
1.2%
76 480
1.2%
75 480
1.2%
74 480
1.2%
73 480
1.2%
72 480
1.2%
71 480
1.2%
70 480
1.2%

cc
Real number (ℝ)

Distinct4015
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69453072
Minimum0
Maximum1
Zeros198
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:44.940871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21940584
Q10.45833333
median0.73913043
Q30.96428571
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.50595238

Descriptive statistics

Standard deviation0.27973032
Coefficient of variation (CV)0.40276162
Kurtosis-1.133288
Mean0.69453072
Median Absolute Deviation (MAD)0.23913043
Skewness-0.44054815
Sum26669.98
Variance0.078249052
MonotonicityNot monotonic
2025-05-22T21:06:45.033813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6536
 
17.0%
0.5 379
 
1.0%
0.9375 333
 
0.9%
0.75 297
 
0.8%
0.3333333333 285
 
0.7%
0.25 238
 
0.6%
0.2222222222 205
 
0.5%
0 198
 
0.5%
0.98 192
 
0.5%
0.96 183
 
0.5%
Other values (4005) 29554
77.0%
ValueCountFrequency (%)
0 198
0.5%
0.04 1
 
< 0.1%
0.04545454545 1
 
< 0.1%
0.05147058824 3
 
< 0.1%
0.05415162455 1
 
< 0.1%
0.0546875 1
 
< 0.1%
0.05555555556 13
 
< 0.1%
0.06034482759 1
 
< 0.1%
0.0625 4
 
< 0.1%
0.06578947368 1
 
< 0.1%
ValueCountFrequency (%)
1 6536
17.0%
0.9949748744 1
 
< 0.1%
0.9917355372 8
 
< 0.1%
0.9913043478 35
 
0.1%
0.9906542056 37
 
0.1%
0.9899497487 1
 
< 0.1%
0.9898989899 26
 
0.1%
0.9893617021 23
 
0.1%
0.9888888889 154
 
0.4%
0.987654321 126
 
0.3%

cr
Real number (ℝ)

Zeros 

Distinct17790
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.053781754
Minimum0
Maximum0.50931677
Zeros2838
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:45.120251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.013888889
median0.036796537
Q30.076338735
95-th percentile0.16770186
Maximum0.50931677
Range0.50931677
Interquartile range (IQR)0.062449846

Descriptive statistics

Standard deviation0.055114982
Coefficient of variation (CV)1.0247896
Kurtosis4.6332322
Mean0.053781754
Median Absolute Deviation (MAD)0.027309865
Skewness1.8278285
Sum2065.2194
Variance0.0030376612
MonotonicityNot monotonic
2025-05-22T21:06:45.213593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2838
 
7.4%
0.1111111111 39
 
0.1%
0.06666666667 38
 
0.1%
0.01408450704 30
 
0.1%
0.04 28
 
0.1%
0.07142857143 26
 
0.1%
0.02564102564 26
 
0.1%
0.003597122302 25
 
0.1%
0.04663212435 24
 
0.1%
0.02222222222 23
 
0.1%
Other values (17780) 35303
91.9%
ValueCountFrequency (%)
0 2838
7.4%
0.0002419549964 1
 
< 0.1%
0.0002470355731 1
 
< 0.1%
0.0002501250625 1
 
< 0.1%
0.0002545824847 1
 
< 0.1%
0.0002630886609 1
 
< 0.1%
0.0002795638803 1
 
< 0.1%
0.0002815315315 1
 
< 0.1%
0.0002926543752 1
 
< 0.1%
0.0003288391976 1
 
< 0.1%
ValueCountFrequency (%)
0.5093167702 2
< 0.1%
0.5018450185 1
< 0.1%
0.4614035088 1
< 0.1%
0.4554455446 1
< 0.1%
0.4552631579 1
< 0.1%
0.4465648855 1
< 0.1%
0.43866171 1
< 0.1%
0.4341463415 1
< 0.1%
0.4329159213 1
< 0.1%
0.4306049822 1
< 0.1%
Distinct115
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:45.554962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length401
Median length134
Mean length109.9375
Min length24

Characters and Unicode

Total characters4221600
Distinct characters106
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowКакие основные мероприятия по предупреждению актов незаконного вмешательства (АНВ) должны проводить эксплуатанты авиации общего назначения (АОН)?
2nd rowКакие меры авиационной безопасности должны быть обеспечены при базировании воздушных судов АОН вне аэропорта?
3rd rowКакие обязанности возложены на командира воздушного судна АОН в части обеспечения авиационной безопасности?
4th rowКакие требования предъявляются к стоянкам воздушных судов АОН в аэропортах?
5th rowКакие действия должен предпринять командир воздушного судна при угрозе или совершении акта незаконного вмешательства (АНВ) в полете?
ValueCountFrequency (%)
the 36480
 
6.0%
or 21840
 
3.6%
a 20880
 
3.4%
of 18720
 
3.1%
какие 15600
 
2.6%
contract 14880
 
2.4%
is 12720
 
2.1%
party 11520
 
1.9%
to 10080
 
1.7%
there 7440
 
1.2%
Other values (628) 440160
72.1%
2025-05-22T21:06:45.819883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
571440
 
13.5%
t 234000
 
5.5%
e 232080
 
5.5%
r 178800
 
4.2%
o 166080
 
3.9%
о 146400
 
3.5%
a 141360
 
3.3%
а 139440
 
3.3%
i 137280
 
3.3%
n 133920
 
3.2%
Other values (96) 2140800
50.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4221600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
571440
 
13.5%
t 234000
 
5.5%
e 232080
 
5.5%
r 178800
 
4.2%
o 166080
 
3.9%
о 146400
 
3.5%
a 141360
 
3.3%
а 139440
 
3.3%
i 137280
 
3.3%
n 133920
 
3.2%
Other values (96) 2140800
50.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4221600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
571440
 
13.5%
t 234000
 
5.5%
e 232080
 
5.5%
r 178800
 
4.2%
o 166080
 
3.9%
о 146400
 
3.5%
a 141360
 
3.3%
а 139440
 
3.3%
i 137280
 
3.3%
n 133920
 
3.2%
Other values (96) 2140800
50.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4221600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
571440
 
13.5%
t 234000
 
5.5%
e 232080
 
5.5%
r 178800
 
4.2%
o 166080
 
3.9%
о 146400
 
3.5%
a 141360
 
3.3%
а 139440
 
3.3%
i 137280
 
3.3%
n 133920
 
3.2%
Other values (96) 2140800
50.7%

vector_variance
Real number (ℝ)

Distinct232
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.91191
Minimum0.077143241
Maximum111.10935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:45.900914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.077143241
5-th percentile0.079921326
Q10.15175958
median0.26034504
Q30.48834705
95-th percentile79.464004
Maximum111.10935
Range111.0322
Interquartile range (IQR)0.33658746

Descriptive statistics

Standard deviation28.19922
Coefficient of variation (CV)1.8910535
Kurtosis0.93176299
Mean14.91191
Median Absolute Deviation (MAD)0.16832765
Skewness1.5751332
Sum572617.36
Variance795.19601
MonotonicityNot monotonic
2025-05-22T21:06:45.991672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07992132591 1040
 
2.7%
0.173160822 1040
 
2.7%
0.4840083043 1040
 
2.7%
0.3626896515 1040
 
2.7%
0.1865662723 1040
 
2.7%
0.1734362256 1040
 
2.7%
79.46400396 1040
 
2.7%
0.3902283311 1040
 
2.7%
0.07946616951 1040
 
2.7%
45.8869955 1040
 
2.7%
Other values (222) 28000
72.9%
ValueCountFrequency (%)
0.07714324136 80
 
0.2%
0.07729976552 80
 
0.2%
0.07821357398 80
 
0.2%
0.07829654835 80
 
0.2%
0.07846629221 80
 
0.2%
0.07880533063 80
 
0.2%
0.07903107492 80
 
0.2%
0.07928715043 80
 
0.2%
0.07937630903 80
 
0.2%
0.07946616951 1040
2.7%
ValueCountFrequency (%)
111.1093453 160
0.4%
110.7273811 80
0.2%
94.49017488 80
0.2%
94.38967905 80
0.2%
94.12368815 80
0.2%
88.45059104 80
0.2%
88.42370726 80
0.2%
88.38578012 80
0.2%
84.98415956 80
0.2%
84.90604036 160
0.4%
Distinct232
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070471059
Minimum-0.062618153
Maximum0.21651461
Zeros0
Zeros (%)0.0%
Negative5840
Negative (%)15.2%
Memory size1.6 MiB
2025-05-22T21:06:46.074276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.062618153
5-th percentile-0.018920056
Q10.0038928799
median0.071038249
Q30.1220813
95-th percentile0.15136574
Maximum0.21651461
Range0.27913276
Interquartile range (IQR)0.11818842

Descriptive statistics

Standard deviation0.063475338
Coefficient of variation (CV)0.90072915
Kurtosis-1.2598261
Mean0.070471059
Median Absolute Deviation (MAD)0.06222072
Skewness-0.016829196
Sum2706.0887
Variance0.0040291185
MonotonicityNot monotonic
2025-05-22T21:06:46.161352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0004430135203 1040
 
2.7%
0.1220813011 1040
 
2.7%
0.1086065015 1040
 
2.7%
0.1270729644 1040
 
2.7%
0.0702169416 1040
 
2.7%
0.1214026773 1040
 
2.7%
0.09871032062 1040
 
2.7%
0.1882752754 1040
 
2.7%
0.00251820812 1040
 
2.7%
-0.01892005579 1040
 
2.7%
Other values (222) 28000
72.9%
ValueCountFrequency (%)
-0.0626181527 80
0.2%
-0.05923939717 80
0.2%
-0.05632451208 80
0.2%
-0.04949530294 80
0.2%
-0.03948069806 80
0.2%
-0.03565941575 80
0.2%
-0.03006410366 160
0.4%
-0.02969175676 80
0.2%
-0.0292655948 80
0.2%
-0.02852717696 80
0.2%
ValueCountFrequency (%)
0.2165146093 80
 
0.2%
0.2112789901 80
 
0.2%
0.2001974916 80
 
0.2%
0.1882752754 1040
2.7%
0.1864865771 80
 
0.2%
0.1853821924 80
 
0.2%
0.1809366633 80
 
0.2%
0.1723285778 80
 
0.2%
0.1696048215 80
 
0.2%
0.1543606066 80
 
0.2%

silhouette_score_cosine
Real number (ℝ)

Distinct232
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11834858
Minimum-0.12532532
Maximum0.36801083
Zeros0
Zeros (%)0.0%
Negative7680
Negative (%)20.0%
Memory size1.6 MiB
2025-05-22T21:06:46.247529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.12532532
5-th percentile-0.048216328
Q10.0036280029
median0.12240976
Q30.21349509
95-th percentile0.26455414
Maximum0.36801083
Range0.49333615
Interquartile range (IQR)0.20986709

Descriptive statistics

Standard deviation0.11261346
Coefficient of variation (CV)0.95154044
Kurtosis-1.2294698
Mean0.11834858
Median Absolute Deviation (MAD)0.10039731
Skewness-0.034003747
Sum4544.5853
Variance0.012681791
MonotonicityNot monotonic
2025-05-22T21:06:46.339664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.003989585577 1040
 
2.7%
0.214045235 1040
 
2.7%
0.1928659341 1040
 
2.7%
0.2228070756 1040
 
2.7%
0.1224003929 1040
 
2.7%
0.2134950944 1040
 
2.7%
0.1414154182 1040
 
2.7%
0.3255402501 1040
 
2.7%
0.0002669048675 1040
 
2.7%
-0.04821632808 1040
 
2.7%
Other values (222) 28000
72.9%
ValueCountFrequency (%)
-0.1253253229 80
0.2%
-0.1217698778 80
0.2%
-0.1091412363 80
0.2%
-0.09655699891 80
0.2%
-0.08375562947 80
0.2%
-0.07406146878 80
0.2%
-0.06889028201 160
0.4%
-0.06612233082 80
0.2%
-0.06264843474 80
0.2%
-0.06216957124 80
0.2%
ValueCountFrequency (%)
0.3680108296 80
 
0.2%
0.3591424579 80
 
0.2%
0.3448955555 80
 
0.2%
0.3255402501 1040
2.7%
0.3224665613 80
 
0.2%
0.3221473413 80
 
0.2%
0.3135232779 80
 
0.2%
0.3001682855 80
 
0.2%
0.2972041818 80
 
0.2%
0.2709473438 80
 
0.2%

mean_LOF
Real number (ℝ)

Distinct145
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99270165
Minimum0.94219653
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:46.427780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.94219653
5-th percentile0.97329773
Q10.99192587
median0.99731363
Q31
95-th percentile1
Maximum1
Range0.057803468
Interquartile range (IQR)0.0080741264

Descriptive statistics

Standard deviation0.010290485
Coefficient of variation (CV)0.010366141
Kurtosis3.6366551
Mean0.99270165
Median Absolute Deviation (MAD)0.0026863667
Skewness-1.908174
Sum38119.743
Variance0.00010589409
MonotonicityNot monotonic
2025-05-22T21:06:46.517486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10160
26.5%
0.997329773 1280
 
3.3%
0.9973136333 1120
 
2.9%
0.9981867634 1120
 
2.9%
0.9979770735 1120
 
2.9%
0.9783616692 1040
 
2.7%
0.9968831169 1040
 
2.7%
0.998492841 1040
 
2.7%
0.9919893191 1040
 
2.7%
0.9732977303 1040
 
2.7%
Other values (135) 18400
47.9%
ValueCountFrequency (%)
0.9421965318 80
0.2%
0.9469026549 80
0.2%
0.9498207885 80
0.2%
0.9514563107 80
0.2%
0.953125 80
0.2%
0.9537572254 80
0.2%
0.960199005 80
0.2%
0.9602385686 80
0.2%
0.9606557377 80
0.2%
0.9607843137 80
0.2%
ValueCountFrequency (%)
1 10160
26.5%
0.9995785925 80
 
0.2%
0.9994897959 80
 
0.2%
0.9992380952 80
 
0.2%
0.9992366412 160
 
0.4%
0.9992266048 80
 
0.2%
0.9992220926 80
 
0.2%
0.9991589571 80
 
0.2%
0.9990796134 80
 
0.2%
0.9989904089 80
 
0.2%

std_LOF
Real number (ℝ)

Zeros 

Distinct149
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.089763326
Minimum0
Maximum0.33506073
Zeros10160
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:46.606741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.073249688
Q30.12681607
95-th percentile0.22954635
Maximum0.33506073
Range0.33506073
Interquartile range (IQR)0.12681607

Descriptive statistics

Standard deviation0.079876459
Coefficient of variation (CV)0.88985627
Kurtosis-0.30924546
Mean0.089763326
Median Absolute Deviation (MAD)0.072034897
Skewness0.73654061
Sum3446.9117
Variance0.0063802487
MonotonicityNot monotonic
2025-05-22T21:06:46.700019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10160
26.5%
0.07302960925 1280
 
3.3%
0.06357484384 1120
 
2.9%
0.07324968814 1120
 
2.9%
0.1997237304 1040
 
2.7%
0.09035060137 1040
 
2.7%
0.07758575841 1040
 
2.7%
0.05017275639 1040
 
2.7%
0.0601929018 1040
 
2.7%
0.1263217749 1040
 
2.7%
Other values (139) 18480
48.1%
ValueCountFrequency (%)
0 10160
26.5%
0.02902821761 80
 
0.2%
0.03193975352 80
 
0.2%
0.03902856678 80
 
0.2%
0.03906577582 80
 
0.2%
0.03906577582 80
 
0.2%
0.03932165141 80
 
0.2%
0.03943614735 80
 
0.2%
0.04100461478 80
 
0.2%
0.04289435876 80
 
0.2%
ValueCountFrequency (%)
0.335060734 80
0.2%
0.3215203916 80
0.2%
0.3127946126 80
0.2%
0.3077838346 80
0.2%
0.3025768239 80
0.2%
0.3005780347 80
0.2%
0.279316793 80
0.2%
0.2791807504 80
0.2%
0.2777418831 80
0.2%
0.2772967769 80
0.2%

dataset
Categorical

Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
bench
19200 
cuad
19200 

Length

Max length5
Median length4.5
Mean length4.5
Min length4

Characters and Unicode

Total characters172800
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbench
2nd rowbench
3rd rowbench
4th rowbench
5th rowbench

Common Values

ValueCountFrequency (%)
bench 19200
50.0%
cuad 19200
50.0%

Length

2025-05-22T21:06:46.779424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:46.822796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bench 19200
50.0%
cuad 19200
50.0%

Most occurring characters

ValueCountFrequency (%)
c 38400
22.2%
b 19200
11.1%
e 19200
11.1%
n 19200
11.1%
h 19200
11.1%
u 19200
11.1%
a 19200
11.1%
d 19200
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 172800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 38400
22.2%
b 19200
11.1%
e 19200
11.1%
n 19200
11.1%
h 19200
11.1%
u 19200
11.1%
a 19200
11.1%
d 19200
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 172800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 38400
22.2%
b 19200
11.1%
e 19200
11.1%
n 19200
11.1%
h 19200
11.1%
u 19200
11.1%
a 19200
11.1%
d 19200
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 172800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 38400
22.2%
b 19200
11.1%
e 19200
11.1%
n 19200
11.1%
h 19200
11.1%
u 19200
11.1%
a 19200
11.1%
d 19200
11.1%

generation_seconds
Real number (ℝ)

Distinct38392
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.563189
Minimum0.42989683
Maximum65.605737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:46.883480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42989683
5-th percentile0.95190233
Q12.5536889
median7.6944836
Q319.287266
95-th percentile36.826844
Maximum65.605737
Range65.17584
Interquartile range (IQR)16.733577

Descriptive statistics

Standard deviation12.88877
Coefficient of variation (CV)1.0259155
Kurtosis2.5440629
Mean12.563189
Median Absolute Deviation (MAD)6.0231396
Skewness1.5617114
Sum482426.46
Variance166.12039
MonotonicityNot monotonic
2025-05-22T21:06:46.973703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.126080513 2
 
< 0.1%
4.604585171 2
 
< 0.1%
0.690433979 2
 
< 0.1%
1.012545824 2
 
< 0.1%
7.52640748 2
 
< 0.1%
1.331415415 2
 
< 0.1%
1.388009071 2
 
< 0.1%
0.9986147881 2
 
< 0.1%
1.309530735 1
 
< 0.1%
2.240381241 1
 
< 0.1%
Other values (38382) 38382
> 99.9%
ValueCountFrequency (%)
0.4298968315 1
< 0.1%
0.4360575676 1
< 0.1%
0.4368202686 1
< 0.1%
0.44257164 1
< 0.1%
0.4426364899 1
< 0.1%
0.4465634823 1
< 0.1%
0.4468970299 1
< 0.1%
0.4474499226 1
< 0.1%
0.4499750137 1
< 0.1%
0.4520859718 1
< 0.1%
ValueCountFrequency (%)
65.60573673 1
< 0.1%
65.33576536 1
< 0.1%
65.32482457 1
< 0.1%
64.8796041 1
< 0.1%
64.76167989 1
< 0.1%
64.4396112 1
< 0.1%
64.23290014 1
< 0.1%
63.49427581 1
< 0.1%
63.28204226 1
< 0.1%
63.27352548 1
< 0.1%

correctness
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
20596 
1
17804 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 20596
53.6%
1 17804
46.4%

Length

2025-05-22T21:06:47.047631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:47.087679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 20596
53.6%
1 17804
46.4%

Most occurring characters

ValueCountFrequency (%)
0 20596
53.6%
1 17804
46.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20596
53.6%
1 17804
46.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20596
53.6%
1 17804
46.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20596
53.6%
1 17804
46.4%

generation_seconds_check
Real number (ℝ)

Distinct38394
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.767107
Minimum0.87416577
Maximum105.67297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:47.154349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.87416577
5-th percentile2.3278589
Q16.7314813
median11.956552
Q318.09479
95-th percentile31.027211
Maximum105.67297
Range104.7988
Interquartile range (IQR)11.363309

Descriptive statistics

Standard deviation9.891827
Coefficient of variation (CV)0.71851167
Kurtosis7.2199123
Mean13.767107
Median Absolute Deviation (MAD)5.5272079
Skewness1.963729
Sum528656.91
Variance97.848242
MonotonicityNot monotonic
2025-05-22T21:06:47.244463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.309538841 2
 
< 0.1%
4.85955286 2
 
< 0.1%
25.92586684 2
 
< 0.1%
2.800643682 2
 
< 0.1%
8.630169392 2
 
< 0.1%
15.20132685 2
 
< 0.1%
3.006390095 1
 
< 0.1%
4.253899336 1
 
< 0.1%
7.488095999 1
 
< 0.1%
8.256672144 1
 
< 0.1%
Other values (38384) 38384
> 99.9%
ValueCountFrequency (%)
0.8741657734 1
< 0.1%
0.8763883114 1
< 0.1%
0.9005186558 1
< 0.1%
0.9077317715 1
< 0.1%
0.918951273 1
< 0.1%
0.9288196564 1
< 0.1%
0.9345691204 1
< 0.1%
0.9448437691 1
< 0.1%
0.9544882774 1
< 0.1%
0.9580488205 1
< 0.1%
ValueCountFrequency (%)
105.6729684 1
< 0.1%
104.1635811 1
< 0.1%
104.1002448 1
< 0.1%
104.0763845 1
< 0.1%
102.9124596 1
< 0.1%
100.1998155 1
< 0.1%
98.06309724 1
< 0.1%
96.29354119 1
< 0.1%
95.98059916 1
< 0.1%
95.80074763 1
< 0.1%

completeness
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.71375
Minimum0
Maximum10
Zeros13705
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:47.313039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3439728
Coefficient of variation (CV)0.90043024
Kurtosis-1.1468089
Mean3.71375
Median Absolute Deviation (MAD)4
Skewness0.31582992
Sum142608
Variance11.182154
MonotonicityNot monotonic
2025-05-22T21:06:47.369824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13705
35.7%
5 8004
20.8%
3 4064
 
10.6%
8 3961
 
10.3%
10 2698
 
7.0%
7 2066
 
5.4%
4 1487
 
3.9%
2 1101
 
2.9%
6 650
 
1.7%
9 569
 
1.5%
ValueCountFrequency (%)
0 13705
35.7%
1 95
 
0.2%
2 1101
 
2.9%
3 4064
 
10.6%
4 1487
 
3.9%
5 8004
20.8%
6 650
 
1.7%
7 2066
 
5.4%
8 3961
 
10.3%
9 569
 
1.5%
ValueCountFrequency (%)
10 2698
 
7.0%
9 569
 
1.5%
8 3961
10.3%
7 2066
 
5.4%
6 650
 
1.7%
5 8004
20.8%
4 1487
 
3.9%
3 4064
10.6%
2 1101
 
2.9%
1 95
 
0.2%

EID
Real number (ℝ)

Distinct232
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.77304
Minimum42.380044
Maximum331.0934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:47.447650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42.380044
5-th percentile52.965112
Q1122.87835
median199.01263
Q3246.11697
95-th percentile296.65554
Maximum331.0934
Range288.71335
Interquartile range (IQR)123.23861

Descriptive statistics

Standard deviation78.739293
Coefficient of variation (CV)0.4171109
Kurtosis-1.0138145
Mean188.77304
Median Absolute Deviation (MAD)60.765086
Skewness-0.3572349
Sum7248884.7
Variance6199.8762
MonotonicityNot monotonic
2025-05-22T21:06:47.533765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195.244217 1040
 
2.7%
230.5020414 1040
 
2.7%
275.3613291 1040
 
2.7%
189.6453343 1040
 
2.7%
296.6555436 1040
 
2.7%
226.5870972 1040
 
2.7%
63.55334358 1040
 
2.7%
166.9979853 1040
 
2.7%
199.0649091 1040
 
2.7%
76.4592612 1040
 
2.7%
Other values (222) 28000
72.9%
ValueCountFrequency (%)
42.38004403 80
0.2%
42.60086856 80
0.2%
44.9588451 80
0.2%
45.25166187 80
0.2%
46.89988419 80
0.2%
48.29775677 80
0.2%
48.7399311 80
0.2%
48.85894606 80
0.2%
49.67930429 80
0.2%
51.12742149 80
0.2%
ValueCountFrequency (%)
331.0933972 80
0.2%
326.2202258 160
0.4%
325.8372361 80
0.2%
320.7560119 80
0.2%
320.2232324 80
0.2%
319.4800753 160
0.4%
317.5430172 80
0.2%
315.8942823 80
0.2%
315.5399059 80
0.2%
313.9760698 160
0.4%

redundancy
Real number (ℝ)

Distinct232
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70433122
Minimum0.32123731
Maximum0.94481765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:47.619281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.32123731
5-th percentile0.40992943
Q10.59089191
median0.72080268
Q30.85413113
95-th percentile0.93103501
Maximum0.94481765
Range0.62358034
Interquartile range (IQR)0.26323922

Descriptive statistics

Standard deviation0.16695017
Coefficient of variation (CV)0.23703361
Kurtosis-0.92077933
Mean0.70433122
Median Absolute Deviation (MAD)0.13173304
Skewness-0.38674464
Sum27046.319
Variance0.02787236
MonotonicityNot monotonic
2025-05-22T21:06:47.706998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4915515182 1040
 
2.7%
0.3997342672 1040
 
2.7%
0.731092452 1040
 
2.7%
0.8147994782 1040
 
2.7%
0.6137297609 1040
 
2.7%
0.4099294345 1040
 
2.7%
0.9172482505 1040
 
2.7%
0.83691603 1040
 
2.7%
0.4816017992 1040
 
2.7%
0.9004436703 1040
 
2.7%
Other values (222) 28000
72.9%
ValueCountFrequency (%)
0.3212373145 80
 
0.2%
0.3279733524 80
 
0.2%
0.329863822 160
 
0.4%
0.3339849071 80
 
0.2%
0.3410457695 80
 
0.2%
0.3574489179 80
 
0.2%
0.3666832633 80
 
0.2%
0.37484406 80
 
0.2%
0.3841816711 80
 
0.2%
0.3997342672 1040
2.7%
ValueCountFrequency (%)
0.944817651 80
0.2%
0.9445301191 80
0.2%
0.9414598371 80
0.2%
0.9410785653 80
0.2%
0.9389324425 80
0.2%
0.9371122959 80
0.2%
0.936536548 80
0.2%
0.9363815806 80
0.2%
0.9353134059 80
0.2%
0.9334278366 80
0.2%

dim
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
768
17280 
1024
10560 
384
10560 

Length

Max length4
Median length3
Mean length3.275
Min length3

Characters and Unicode

Total characters125760
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1024
2nd row1024
3rd row1024
4th row1024
5th row1024

Common Values

ValueCountFrequency (%)
768 17280
45.0%
1024 10560
27.5%
384 10560
27.5%

Length

2025-05-22T21:06:47.783076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:47.834321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
768 17280
45.0%
1024 10560
27.5%
384 10560
27.5%

Most occurring characters

ValueCountFrequency (%)
8 27840
22.1%
4 21120
16.8%
7 17280
13.7%
6 17280
13.7%
1 10560
 
8.4%
0 10560
 
8.4%
2 10560
 
8.4%
3 10560
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 27840
22.1%
4 21120
16.8%
7 17280
13.7%
6 17280
13.7%
1 10560
 
8.4%
0 10560
 
8.4%
2 10560
 
8.4%
3 10560
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 27840
22.1%
4 21120
16.8%
7 17280
13.7%
6 17280
13.7%
1 10560
 
8.4%
0 10560
 
8.4%
2 10560
 
8.4%
3 10560
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 27840
22.1%
4 21120
16.8%
7 17280
13.7%
6 17280
13.7%
1 10560
 
8.4%
0 10560
 
8.4%
2 10560
 
8.4%
3 10560
 
8.4%

k1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1.2
25600 
1.0
6400 
1.5
6400 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters115200
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.2
2nd row1.2
3rd row1.2
4th row1.2
5th row1.2

Common Values

ValueCountFrequency (%)
1.2 25600
66.7%
1.0 6400
 
16.7%
1.5 6400
 
16.7%

Length

2025-05-22T21:06:47.889894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:47.933670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.2 25600
66.7%
1.0 6400
 
16.7%
1.5 6400
 
16.7%

Most occurring characters

ValueCountFrequency (%)
1 38400
33.3%
. 38400
33.3%
2 25600
22.2%
0 6400
 
5.6%
5 6400
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 115200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 38400
33.3%
. 38400
33.3%
2 25600
22.2%
0 6400
 
5.6%
5 6400
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 115200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 38400
33.3%
. 38400
33.3%
2 25600
22.2%
0 6400
 
5.6%
5 6400
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 115200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 38400
33.3%
. 38400
33.3%
2 25600
22.2%
0 6400
 
5.6%
5 6400
 
5.6%

c
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
60
19200 
1
4800 
2
4800 
4
4800 
8
4800 

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters57600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
60 19200
50.0%
1 4800
 
12.5%
2 4800
 
12.5%
4 4800
 
12.5%
8 4800
 
12.5%

Length

2025-05-22T21:06:47.992689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:48.043588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
60 19200
50.0%
1 4800
 
12.5%
2 4800
 
12.5%
4 4800
 
12.5%
8 4800
 
12.5%

Most occurring characters

ValueCountFrequency (%)
6 19200
33.3%
0 19200
33.3%
1 4800
 
8.3%
2 4800
 
8.3%
4 4800
 
8.3%
8 4800
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 19200
33.3%
0 19200
33.3%
1 4800
 
8.3%
2 4800
 
8.3%
4 4800
 
8.3%
8 4800
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 19200
33.3%
0 19200
33.3%
1 4800
 
8.3%
2 4800
 
8.3%
4 4800
 
8.3%
8 4800
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 19200
33.3%
0 19200
33.3%
1 4800
 
8.3%
2 4800
 
8.3%
4 4800
 
8.3%
8 4800
 
8.3%

mean_relevance
Real number (ℝ)

Zeros 

Distinct20598
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23528562
Minimum0
Maximum0.92713993
Zeros2838
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-05-22T21:06:48.120041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13104202
median0.21353787
Q30.32237899
95-th percentile0.52343786
Maximum0.92713993
Range0.92713993
Interquartile range (IQR)0.19133697

Descriptive statistics

Standard deviation0.15344234
Coefficient of variation (CV)0.65215348
Kurtosis0.89290095
Mean0.23528562
Median Absolute Deviation (MAD)0.093503514
Skewness0.81069094
Sum9034.9679
Variance0.023544551
MonotonicityNot monotonic
2025-05-22T21:06:48.213093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2838
 
7.4%
0.1 57
 
0.1%
0.8184111623 24
 
0.1%
0.359656041 18
 
< 0.1%
0.2235899936 18
 
< 0.1%
0.1610348054 18
 
< 0.1%
0.1211069558 18
 
< 0.1%
0.1803228372 18
 
< 0.1%
0.3413432339 18
 
< 0.1%
0.6980892387 18
 
< 0.1%
Other values (20588) 35355
92.1%
ValueCountFrequency (%)
0 2838
7.4%
0.004017857143 1
 
< 0.1%
0.004403794038 1
 
< 0.1%
0.004638619202 2
 
< 0.1%
0.004976141786 1
 
< 0.1%
0.005012771392 1
 
< 0.1%
0.005264957265 1
 
< 0.1%
0.005298913043 3
 
< 0.1%
0.005347068923 1
 
< 0.1%
0.005769230769 1
 
< 0.1%
ValueCountFrequency (%)
0.9271399257 1
 
< 0.1%
0.924975221 1
 
< 0.1%
0.922040848 1
 
< 0.1%
0.9215007466 13
< 0.1%
0.8979968115 13
< 0.1%
0.8811435031 1
 
< 0.1%
0.8601624764 1
 
< 0.1%
0.8587049073 10
< 0.1%
0.8543704293 1
 
< 0.1%
0.8519144886 1
 
< 0.1%

dataset_label
Categorical

Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
19200 
0
19200 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 19200
50.0%
0 19200
50.0%

Length

2025-05-22T21:06:48.287708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:48.327285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 19200
50.0%
0 19200
50.0%

Most occurring characters

ValueCountFrequency (%)
1 19200
50.0%
0 19200
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19200
50.0%
0 19200
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19200
50.0%
0 19200
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19200
50.0%
0 19200
50.0%

chunk_strategy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
23040 
1
15360 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23040
60.0%
1 15360
40.0%

Length

2025-05-22T21:06:48.373608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:48.413777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 23040
60.0%
1 15360
40.0%

Most occurring characters

ValueCountFrequency (%)
0 23040
60.0%
1 15360
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23040
60.0%
1 15360
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23040
60.0%
1 15360
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23040
60.0%
1 15360
40.0%

chunk_len
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.9
Minimum2
Maximum2048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2025-05-22T21:06:48.453480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.9
Q18
median24
Q3256
95-th percentile1075.2
Maximum2048
Range2046
Interquartile range (IQR)248

Descriptive statistics

Standard deviation478.92856
Coefficient of variation (CV)1.9556086
Kurtosis7.4352461
Mean244.9
Median Absolute Deviation (MAD)20
Skewness2.8235866
Sum9404160
Variance229372.56
MonotonicityNot monotonic
2025-05-22T21:06:48.514040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
16 7680
20.0%
256 7680
20.0%
8 5760
15.0%
32 3840
10.0%
4 3840
10.0%
2 1920
 
5.0%
1024 1920
 
5.0%
128 1920
 
5.0%
2048 1920
 
5.0%
512 1920
 
5.0%
ValueCountFrequency (%)
2 1920
 
5.0%
4 3840
10.0%
8 5760
15.0%
16 7680
20.0%
32 3840
10.0%
128 1920
 
5.0%
256 7680
20.0%
512 1920
 
5.0%
1024 1920
 
5.0%
2048 1920
 
5.0%
ValueCountFrequency (%)
2048 1920
 
5.0%
1024 1920
 
5.0%
512 1920
 
5.0%
256 7680
20.0%
128 1920
 
5.0%
32 3840
10.0%
16 7680
20.0%
8 5760
15.0%
4 3840
10.0%
2 1920
 
5.0%

stride
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.5
19840 
0.25
10240 
0.1
8320 

Length

Max length4
Median length3
Mean length3.2666667
Min length3

Characters and Unicode

Total characters125440
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.1
3rd row0.1
4th row0.1
5th row0.1

Common Values

ValueCountFrequency (%)
0.5 19840
51.7%
0.25 10240
26.7%
0.1 8320
21.7%

Length

2025-05-22T21:06:48.573766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:48.625025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.5 19840
51.7%
0.25 10240
26.7%
0.1 8320
21.7%

Most occurring characters

ValueCountFrequency (%)
0 38400
30.6%
. 38400
30.6%
5 30080
24.0%
2 10240
 
8.2%
1 8320
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 38400
30.6%
. 38400
30.6%
5 30080
24.0%
2 10240
 
8.2%
1 8320
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 38400
30.6%
. 38400
30.6%
5 30080
24.0%
2 10240
 
8.2%
1 8320
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 38400
30.6%
. 38400
30.6%
5 30080
24.0%
2 10240
 
8.2%
1 8320
 
6.6%

embedder
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
10560 
1
10560 
2
8640 
3
8640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38400
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10560
27.5%
1 10560
27.5%
2 8640
22.5%
3 8640
22.5%

Length

2025-05-22T21:06:48.679972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T21:06:48.727712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10560
27.5%
1 10560
27.5%
2 8640
22.5%
3 8640
22.5%

Most occurring characters

ValueCountFrequency (%)
0 10560
27.5%
1 10560
27.5%
2 8640
22.5%
3 8640
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10560
27.5%
1 10560
27.5%
2 8640
22.5%
3 8640
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10560
27.5%
1 10560
27.5%
2 8640
22.5%
3 8640
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10560
27.5%
1 10560
27.5%
2 8640
22.5%
3 8640
22.5%

Interactions

2025-05-22T21:06:43.030599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:28.065828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:29.165631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:30.194121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:31.345412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:32.369779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:33.448236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:34.460534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:35.521527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:36.746662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:37.805070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:38.822723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:39.840599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:40.842482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:42.034060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:43.093518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:28.141882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:29.233668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:30.266227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:31.416193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:32.447375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:33.515707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:34.537023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:35.593940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:36.822797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:37.877648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:38.893397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:39.906765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:40.926076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:42.106766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:43.160688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:28.214446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:29.300371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:30.333130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:31.479787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:32.513725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:33.585923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:34.599902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:35.668186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:36.893278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:37.940566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:38.959956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:39.973422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:41.142847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:42.169321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:43.222944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:28.286159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-22T21:06:29.367069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

filequestion_idcccrquestionvector_variancesilhouette_score_euclidiansilhouette_score_cosinemean_LOFstd_LOFdatasetgeneration_secondscorrectnessgeneration_seconds_checkcompletenessEIDredundancydimk1cmean_relevancedataset_labelchunk_strategychunk_lenstrideembedder
0S_16_0.1_BAAI00.9436620.037388Какие основные мероприятия по предупреждению актов незаконного вмешательства (АНВ) должны проводить эксплуатанты авиации общего назначения (АОН)?0.4046260.0946610.1684490.9961980.087121bench2.48558215.1076159.0206.0079520.7988210241.2600.27218110160.10
1S_16_0.1_BAAI10.9452050.037602Какие меры авиационной безопасности должны быть обеспечены при базировании воздушных судов АОН вне аэропорта?0.4046260.0946610.1684490.9961980.087121bench1.15636114.1460448.0206.0079520.7988210241.2600.25632510160.10
2S_16_0.1_BAAI20.8717950.036500Какие обязанности возложены на командира воздушного судна АОН в части обеспечения авиационной безопасности?0.4046260.0946610.1684490.9961980.087121bench16.79421305.0725784.0206.0079520.7988210241.2600.25940410160.10
3S_16_0.1_BAAI30.9629630.027426Какие требования предъявляются к стоянкам воздушных судов АОН в аэропортах?0.4046260.0946610.1684490.9961980.087121bench20.81736713.9016858.0206.0079520.7988210241.2600.27670010160.10
4S_16_0.1_BAAI40.9710140.039739Какие действия должен предпринять командир воздушного судна при угрозе или совершении акта незаконного вмешательства (АНВ) в полете?0.4046260.0946610.1684490.9961980.087121bench8.88240313.0360518.0206.0079520.7988210241.2600.25534710160.10
5S_16_0.1_BAAI50.9686270.150152Какие требования предъявляются к обладателю свидетельства частного пилота самолета?0.4046260.0946610.1684490.9961980.087121bench27.46229907.5833633.0206.0079520.7988210241.2600.30083010160.10
6S_16_0.1_BAAI60.9687500.010835Каковы возрастные ограничения для командира воздушного судна, занятого в международных коммерческих воздушных перевозках?0.4046260.0946610.1684490.9961980.087121bench28.74750719.8945565.0206.0079520.7988210241.2600.44687410160.10
7S_16_0.1_BAAI70.9230770.013001Какие квалификационные отметки могут быть внесены в свидетельство пилота сверхлегкого воздушного судна?0.4046260.0946610.1684490.9961980.087121bench4.61405514.8791005.0206.0079520.7988210241.2600.39062710160.10
8S_16_0.1_BAAI80.9458480.154755Какие требования предъявляются к обладателю свидетельства линейного пилота самолета?0.4046260.0946610.1684490.9961980.087121bench26.14942612.6632948.0206.0079520.7988210241.2600.22001610160.10
9S_16_0.1_BAAI91.0000000.002983Какие требования предъявляются к медицинскому заключению для обладателя свидетельства бортпроводника?0.4046260.0946610.1684490.9961980.087121bench27.59803411.6590565.0206.0079520.7988210241.2600.58287010160.10
filequestion_idcccrquestionvector_variancesilhouette_score_euclidiansilhouette_score_cosinemean_LOFstd_LOFdatasetgeneration_secondscorrectnessgeneration_seconds_checkcompletenessEIDredundancydimk1cmean_relevancedataset_labelchunk_strategychunk_lenstrideembedder
19190W_256_0.1_intfloat700.6883120.126492Does the contract limit the ability of a party to transfer the license being granted to a third party?0.1734360.1214030.2134950.9981870.060193cuad4.992281117.6972037.0226.5870970.4099293841.580.186825012560.11
19191W_256_0.1_intfloat710.5542170.095634Does the contract include a cap on liability upon the breach of a party’s obligation? This includes time limitation for the counterparty to bring claims or maximum amount for recovery.0.1734360.1214030.2134950.9981870.060193cuad4.460717117.3244957.0226.5870970.4099293841.580.142617012560.11
19192W_256_0.1_intfloat720.6611570.106383Does the contract contain a clause that would award either party liquidated damages for breach or a fee upon the termination of a contract (termination fee)?0.1734360.1214030.2134950.9981870.060193cuad3.25410409.6416113.0226.5870970.4099293841.580.156042012560.11
19193W_256_0.1_intfloat730.5750000.030831Is there any clause providing for joint or shared ownership of intellectual property between the parties to the contract?0.1734360.1214030.2134950.9981870.060193cuad3.138813016.5594064.0226.5870970.4099293841.580.221488012560.11
19194W_256_0.1_intfloat741.0000000.000000Is consent or notice required of a party if the contract is assigned to a third party?0.1734360.1214030.2134950.9981870.060193cuad4.771574010.7712450.0226.5870970.4099293841.580.000000012560.11
19195W_256_0.1_intfloat750.6956520.018735Does one party have the right to terminate or is consent or notice required of the counterparty if such party undergoes a change of control, such as a merger, stock sale, transfer of all or substantially all of its assets or business, or assignment by operation of law?0.1734360.1214030.2134950.9981870.060193cuad4.868158018.6185410.0226.5870970.4099293841.580.390829012560.11
19196W_256_0.1_intfloat760.6174240.187356Is there a restriction on the ability of a party to compete with the counterparty or operate in a certain geography or business or technology sector?0.1734360.1214030.2134950.9981870.060193cuad4.790033118.3427637.0226.5870970.4099293841.580.274978012560.11
19197W_256_0.1_intfloat771.0000000.000000Is there a restriction on the ability of a party to compete with the counterparty or operate in a certain geography or business or technology sector?0.1734360.1214030.2134950.9981870.060193cuad4.541761013.9234200.0226.5870970.4099293841.580.000000012560.11
19198W_256_0.1_intfloat780.7467530.119418Is there a clause granting one party a right of first refusal, right of first offer or right of first negotiation to purchase, license, market, or distribute equity interest, technology, assets, products or services?0.1734360.1214030.2134950.9981870.060193cuad4.74166318.2908058.0226.5870970.4099293841.580.161935012560.11
19199W_256_0.1_intfloat790.6083330.092522Is consent or notice required of a party if the contract is assigned to a third party?0.1734360.1214030.2134950.9981870.060193cuad4.772182115.5666325.0226.5870970.4099293841.580.166511012560.11